Data Scientist, Clinical Informatics

Verily is an Alphabet company combining a data-driven, people-first approach to bring the promise of precision health to everyone, every day.

Our team combines expertise in healthcare, data science and technology to improve the health and well-being of our communities. We are developing the infrastructure and solutions to harness the profusion of health information for good. Our data-driven solutions span three primary areas: research, care and innovation. Programs include Project Baseline - our research initiative to increase participation and evidence generation in clinical research; Onduo - our personalized virtual care platform, which includes connected tools, lifestyle coaching and clinical support; and Debug - our effort to reduce the threat of mosquito-borne diseases by combining machine learning with sterile insect technique. We’re also actively working to combat the spread of COVID-19 through new programs like Healthy at Work. 

Description

Our Data Science group specializes in analyzing and building models to help make sense of large datasets resulting from bio-sensors, digital pathology, clinical informatics, molecular assays and patient surveys. We combine domain knowledge and programming expertise with statistical and machine learning skills to build scalable models and solutions that help power Verily’s various product areas. 

In this position, you will work cross-functionally with Verily's clinical, software, product and science teams to develop novel machine learning and statistical models in the context of clinical informatics. To enable real-world impact, you will deliver key analyses for motivating product direction and work closely with health system partners  to deliver user-facing tools and insights.

Responsibilities

• Work with large, complex data sets. Solve difficult, non-routine analysis problems, handling data challenges from a real-world setting.
• Develop quality control and pre-processing tools for a broad range of digital and clinical data types.
• Iterate on, engineer key features and build new statistical and machine learning models relating measured features to clinical endpoints.
• Communicate highly technical results and methods clearly, as well as, interact cross-functionally with a wide variety of people and teams.

Qualifications

• Ph.D degree in a quantitative discipline (e.g., statistics, computer science, applied mathematics, or similar) or equivalent practical experience.
• Demonstrated experience working with Python.
• Demonstrated experience working with various data source from EHR (clinical notes, workflow data, order set, etc).
• Experience with exploratory and statistical data analysis (such as linear models, multivariate analysis, predictive modeling and deep learning).

• Experience with delivering data driven  insights for clinical workflow optimization.
• Demonstrated willingness to both teach others and learn new techniques.
• Experience with medical terminologies and ontologies, clinical natural language processing.